
import numpy as np
import pandas as pd
import geopandas as gpd
import folium
from folium import plugins
import json
import datetime
import branca
from sklearn.preprocessing import MinMaxScaler

import plotly.express as ply
#These lines set up the plotting functionality and formatting.
import matplotlib
#matplotlib.use('Agg', warn=False)
#%matplotlib inline
import matplotlib.pyplot as plots
#plots.style.use('fivethirtyeight')
# import warnings
# warnings.simplefilter(action="ignore", category=FutureWarning)


with open ("data/cityboundary.geojson") as f:
    citylayer = json.load(f)

city_cville_coord=[38.0393, -78.4767]
map_with_citylayer = folium.Map(location=city_cville_coord, zoom_start=13)
folium.GeoJson(citylayer).add_to(map_with_citylayer)
map_with_citylayer


sales = pd.read_csv('data/Real_Estate__Sales_.csv')
sales


sales['Address'] = sales['StreetNumber'] + ' '+sales['StreetName'] 
sales = sales.drop(columns=['StreetNumber','StreetName'])
sales


def formatDate(row):  
    date = row['SaleDate']
    return date[:10].replace('/','-')

sales = sales[~pd.isna(sales.SaleDate)]
sales = sales.assign(SaleDate= sales.apply(formatDate,axis=1))
#sales['SaleDate'] = pd.DatetimeIndex(sales['SaleDate'])
sales


resid = pd.read_csv('data/Real_Estate__Residential_Details_.csv',header=0)
resid


# Filter out non-residential parcels
labels = {'Single Family', 'Duplex', 'Single Family Attached', 'Condominium',
          'Single Family-1 Conversion', 'Triplex', 'Rooming House', 'Condo Main',
          'Single Family-3 Conversion', 'Four-Level Split', 'Three-Level Split',
          'Single Family-2 Conversion', 'Condominium-Flex', 'Quadplex'}

salesResid = pd.merge(sales,resid[resid.UseCode.isin(labels)].loc[:,['ParcelNumber','UseCode']],on="ParcelNumber")
salesResid


geocoded = pd.read_csv('data/coordinates.csv')

geoSalesResid = pd.merge(salesResid,geocoded,left_on="Address",right_on="ADDRESS")
geoSalesResid = geoSalesResid[geoSalesResid.SaleAmount > 100].drop(columns=["ADDRESS"])
geoSalesResid = geoSalesResid.drop_duplicates(subset=['ParcelNumber','SaleDate','SaleAmount'])
geoSalesResid


def makeRecentSales(cutoffDate):
    return geoSalesResid[(geoSalesResid.SaleDate > cutoffDate) & (geoSalesResid.SaleDate < '2020-01-01')]

recentSales = makeRecentSales('2018-12-31')


def formatSalesNumber(v):
    # split it between left/right of decimal point
    splitstr = v.split('.')
    
    # left num is first entry of split list, then count number of digits it has
    leftNum = splitstr[0]
    leftNumDigits = len(splitstr[0])
    
    # if there exists num right of point, then set it to right Num
    if len(splitstr) > 1:  
        rightNum = '.' + splitstr[1]
    else:
        rightNum = '.00'
    
    # Count number of commas necessary
    comma = 0
    if leftNumDigits > 3:
        temp = leftNumDigits
        while(((temp -1) // 3) > 0):
            temp -= 3 
            comma += 1
    else:
        return '$' + leftNum + rightNum

    # Produce the string
    if leftNumDigits % 3 == 0:
        s = '$' + leftNum[:3]
        for i in np.arange(comma):
            a = i + 1
            s += ','+ leftNum[3 * a:3 * a + 3]
        return s + rightNum
    else:
        s = '$' + leftNum[:(leftNumDigits % 3)]
        for i in np.arange(comma):
            a = i + 1 
            b = leftNumDigits % 3 
            s += ','+ leftNum[b * a : b * a + 3]
        return s + rightNum


def chooseColorSale(sale):
    if sale < 89000:
        return '#fef0d9'
    elif 89000<= sale < 150000:
        return '#fdd49e'
    elif 150000 <= sale < 300000:
        return '#fdbb84'
    elif 300000 <= sale < 500000:
        return '#fc8d59'
    elif 500000 <= sale < 800000:
        return '#e34a33'
    else:
        return '#b30000'

def addMarkersSale(row,currmap):
    line = row["SaleDate"][:] + ": " + formatSalesNumber(str(row['SaleAmount']))
    folium.CircleMarker(location=[row['LATITUDE'],row['LONGITUDE']],
                  popup=line,radius=1.5,color=chooseColorSale(row['SaleAmount']),
                       fill_color=chooseColorSale(row['SaleAmount'])).add_to(currmap)
    return


def generateSalesMap():
    m = folium.Map(location=city_cville_coord, zoom_start=13)
    makeRecentSales('2018-12-31').apply(addMarkersSale,axis=1,args=(m,))
    return m
generateSalesMap()


def generateJSONMap():
    m = folium.Map(location=city_cville_coord, zoom_start=13)
    folium.GeoJson("data/regions.geojson", name='area').add_to(m)
    return m
generateJSONMap()


layoutFile = json.load(open('data/regions.geojson'))
arr = []
for i in np.arange(len(layoutFile['features'])):
    arr.append(layoutFile['features'][i]['geometry']['coordinates'][0])
lay = pd.Series(arr)


def on_segment(p,q,r):
    if r[0] <= max(p[0], q[0]) and r[0] >= min(p[0], q[0]) and r[1] <= max(p[1], q[1]) and r[1] >= min(p[1], q[1]):
        return True
    else: 
        return False

def orientation(p,q,r):
    val = ((q[1] - p[1]) * (r[0] - q[0])) - ((q[0] - p[0]) * (r[1] - q[1]))
    if val == 0:
        return 0
    elif val > 0:
        return 1
    else:
        return -1

def intersects(s1,s2):
    p1,q1 =s1
    p2,q2 = s2
    
    o1 = orientation(p1,q1,p2)
    o2 = orientation(p1,q1,q2)
    o3 = orientation(p2,q2,p1)    
    o4 = orientation(p2,q2,q1)
    
    if o1 != o2 and o3 != o4:
        return True
    if o1 == 0 and on_segment(p1,q1,p2):
        return True
    if o2 == 0 and on_segment(p1, q1, q2):
        return True
    if o3 == 0 and on_segment(p2, q2, p1):
        return True
    if o4 == 0 and on_segment(p2, q2, q1):
        return True
    return False


# defines a point, returns a series of whether or not point is in each JSON polygon
def pointinpolygons(row,layout):
    pt = (row['LATITUDE'],row['LONGITUDE'])
    rayseg = makeray(pt)
    
    foundin = layout.apply(pointinpoly,args=(rayseg,))
    if foundin[foundin].shape[0] == 0:
        return 0
    else:
        return foundin[foundin].index[0] + 1
    

# produces a ray by extending a point horizontally depending on its longitude relative to Cville
def makeray(point):
    # Bounds for Longitude 
    leftb = -78.647930
    rightb = -78.411250
    midb = leftb + (rightb-leftb)/2

    if point[1] > midb:
        return (point,(point[0],leftb)) 
    else:
        return (point,(point[0],rightb))
    
# determines if the point is in or not in a single JSON polygon
def pointinpoly(polygons,raysegment):
    # Makes pairs of indices to form polygon edges (assumes that edges are defined as) from a polygon that has n sides
    # 0 to 1, 1 to 2, 2 to 3, etc... until the last index which would be n-1 to 0
    combo = []
    for i in np.arange(len(polygons)):
        if i < len(polygons) - 1:
            apair = (i,i+1)
        else:
            apair = (i,0)
        combo.append(apair)
    
    # Iterates through the list of pairs and indexes into the polygons variable which is a list of lists. It produces a 
    # a segment represented by two points and it is determined whether it intersects with the address.
    intersections = 0 
    for pair in combo:
        segment1 = ((polygons[pair[0]][1],polygons[pair[0]][0]), (polygons[pair[1]][1],polygons[pair[1]][0]))
        if intersects(segment1,raysegment):
            intersections +=1
            
    if intersections % 2 == 0:
        return False
    else:
        return True


recentSales.insert(recentSales.shape[1],'Region',recentSales.apply(pointinpolygons,axis=1,args=(lay,)))


def generateSalesMapWithJson():
    m = folium.Map(location=city_cville_coord, zoom_start=13)
    testTab = recentSales[recentSales.Region >0].apply(addMarkersSale,axis=1,args=(m,))
    folium.GeoJson("data/regions.geojson", name='area').add_to(m)
    return m

generateSalesMapWithJson()


salesByRegion = recentSales.groupby(['Region']).mean().loc[:,"SaleAmount"]
salesByRegion

Region
1     717857.860465
2     511822.775000
3     314556.120000
4     368137.540230
5     441875.581395
6     200313.800000
7     314220.600000
8     241635.603448
9     357772.259259
10    292720.743590
11    750000.000000
12    105000.000000
13    328184.814516
14    358465.073171
15    767669.529412
16    586843.750000
17    278134.142857
18    207666.666667
19    563986.363636
Name: SaleAmount, dtype: float64


def classifyHouses(df):
    df.insert(df.shape[1],'Region',df.apply(pointinpolygons,axis=1,args=(lay,)))
    df.to_csv('data/salesLast20Years.csv')
    return 

run_already = True
if not run_already:
    classifyHouses(makeRecentSales('1999-12-31'))


def stylefunction(x):  
    sale=x['properties']['SaleAverage']
    if sale < 89000:
        color= '#fef0d9'
    elif 89000<= sale < 150000:
        color= '#fdd49e'
    elif 150000 <= sale < 300000:
        color= '#fdbb84'
    elif 300000 <= sale < 500000:
        color= '#fc8d59'
    elif 500000 <= sale < 800000:
        color= '#e34a33'
    else:
        color= '#b30000'
    return {'weight': 5, 'color': color,'fill': True, 'fillOpacity':0.5}


def produceAGroup(t,func,label):
    if func == 'mean':
        temp = t.groupby(['Region']).mean().loc[:,label]
        return pd.DataFrame({'Regions': pd.Series(data=np.arange(20)), 'Avg': temp}).fillna(value=0)
    elif func == 'median':
        temp = t.groupby(['Region']).median().loc[:,label]
        return pd.DataFrame({'Regions': pd.Series(data=np.arange(20)), 'Med': temp}).fillna(value=0)          
    elif func == 'count':
        temp = t.groupby(['Region']).count().iloc[:,0]
        return pd.DataFrame({'Regions': pd.Series(data=np.arange(20)), 'Count': temp}).fillna(value=0)
    

def checkIfZero(s,l,n):
    try:
        return str(round(s[l][n],2))
    except:
        return '0'

def addMarkersSaleGroup(row,currmap,group):
    # normalized to day (percNorm)
    price = row['SaleAmount']
    line = row ["UseCode"] + '(' + row["SaleDate"][:4] + "): " + formatSalesNumber(str(row['SaleAmount']))
    marker = folium.CircleMarker(location=[row['LATITUDE'],row['LONGITUDE']],
          popup=line,radius=1.5,color=chooseColorSale(price),
               fill_color=chooseColorSale(price))
    group.add_child(marker)
    return


def generateSalesMap():
    # 1. Import the saved Table
    salesTable = pd.read_csv('data/salesLast20Years.csv') 
    
    # 2. Drop the redundant column
    salesTable = salesTable.drop(columns='Unnamed: 0')
    
    # 3. Group by region number and find average sale amount and median in each, even for region 0. Produce a DataFrame that has
    # the region numbers as a column
    salesByRegionAvg = produceAGroup(salesTable,'mean','SaleAmount')
    salesByRegionMed = produceAGroup(salesTable,'median','SaleAmount')
    salesByRegionCount = produceAGroup(salesTable,'count','SaleAmount')
    
    # 4. load GeoJSON file of regions, adding the SaleAverage as a property to each
    with open ("data/regions.geojson") as f:
        regionlayer = json.load(f)
        count = 1
        for i in regionlayer['features']:
            i['properties']['AVG PRICE'] = formatSalesNumber(checkIfZero(salesByRegionAvg,'Avg',count))
            i['properties']['MED PRICE'] = formatSalesNumber(checkIfZero(salesByRegionMed,'Med',count))
            i['properties']['NUM SALES'] = salesByRegionCount.Count[count]
            count += 1
            
    
    # 5. Load GeoJSON of city boundary, adding SaleAverage/Median/Count of whole city to the Layer
    with open ("data/cityboundary.geojson") as f:
        citylayer = json.load(f)
    citylayer['features'][0]['properties']['AVG PRICE'] = formatSalesNumber(str(round(salesTable.SaleAmount.mean(),2)))
    citylayer['features'][0]['properties']['MED PRICE'] = formatSalesNumber(str(round(salesTable.SaleAmount.median(),2)))
    citylayer['features'][0]['properties']['NUM SALES'] = salesTable.shape[0]

        
    # 6. Create Folium Map and add TileLayer Options      
    m = plugins.DualMap(location=city_cville_coord, zoom_start=13)
    folium.raster_layers.TileLayer('Open Street Map').add_to(m)
    folium.raster_layers.TileLayer('Stamen Toner').add_to(m)
    folium.raster_layers.TileLayer('CartoDB Positron').add_to(m)

    # 7. Group: GeoJson of Charlottesville City Boundary
    cityavg = salesTable.SaleAmount.mean()
    style_function = lambda x: {'fillColor': chooseColorSale(cityavg), 'color': chooseColorSale(cityavg)}
    city = folium.GeoJson(citylayer, name='City of Charlottesville Boundary',
                   style_function=style_function,
                         tooltip=folium.GeoJsonTooltip(['NAME','AVG PRICE','MED PRICE', 'NUM SALES'])).add_to(m)

    # 8. Group: Addition of Markers for each individual Parcel
    saleMarkers = folium.FeatureGroup(name='Sale Price Markers',show=False)
    salesTable.apply(addMarkersSaleGroup,axis=1,args=(m,saleMarkers))
    m.add_child(saleMarkers)

    # 9. Group: Choropleth of Planning Neighborhood Regions' Average Sale
    choroAvg = folium.Choropleth(geo_data=regionlayer, key_on='feature.properties.OBJECTID',
                      data=salesByRegionAvg,columns=['Regions','Avg'],
                      fill_color='YlOrRd',legend_name='Sale Amount ($): Average',name='Sale Amount Average', show=False,
                      
                      highlight=True).add_to(m.m1)
    choroAvg.geojson.add_child(folium.GeoJsonTooltip(['OBJECTID','NAME','AVG PRICE','NUM SALES']))
 
    # 10. Group: Choropleth of Planning Neighborhood Regions' Median Sale
    choroMed = folium.Choropleth(geo_data=regionlayer, key_on='feature.properties.OBJECTID',
                      data=salesByRegionMed,columns=['Regions','Med'],
                      fill_color='YlOrRd',legend_name='Sale Amount ($): Median',name='Sale Amount Median', show=False,
                      highlight=True).add_to(m.m2)
    choroMed.geojson.add_child(folium.GeoJsonTooltip(['OBJECTID','NAME','MED PRICE','NUM SALES']))
    
    
    # 11. Add LayerControl and Save Map
    folium.LayerControl().add_to(m)
    m.save("figures/map-sales.html")
    return 

generateSalesMap()


def generateSalesMapSeparate():
    # 1. Import the saved Table
    salesTable = pd.read_csv('data/salesLast20Years.csv') 
    
    # 2. Drop the redundant column
    salesTable = salesTable.drop(columns='Unnamed: 0')
    
    # 3. Group by region number and find average sale amount and median in each, even for region 0. Produce a DataFrame that has
    # the region numbers as a column
    salesByRegionAvg = produceAGroup(salesTable,'mean','SaleAmount')
    salesByRegionMed = produceAGroup(salesTable,'median','SaleAmount')
    salesByRegionCount = produceAGroup(salesTable,'count','SaleAmount')
    
    # 4. load GeoJSON file of regions, adding the SaleAverage as a property to each
    with open ("data/regions.geojson") as f:
        regionlayer = json.load(f)
        count = 1
        for i in regionlayer['features']:
            i['properties']['AVG PRICE'] = formatSalesNumber(checkIfZero(salesByRegionAvg,'Avg',count))
            i['properties']['MED PRICE'] = formatSalesNumber(checkIfZero(salesByRegionMed,'Med',count))
            i['properties']['NUM SALES'] = salesByRegionCount.Count[count]
            count += 1
            
    
    # 5. Load GeoJSON of city boundary, adding SaleAverage/Median/Count of whole city to the Layer
    with open ("data/cityboundary.geojson") as f:
        citylayer = json.load(f)
    citylayer['features'][0]['properties']['AVG PRICE'] = formatSalesNumber(str(round(salesTable.SaleAmount.mean(),2)))
    citylayer['features'][0]['properties']['MED PRICE'] = formatSalesNumber(str(round(salesTable.SaleAmount.median(),2)))
    citylayer['features'][0]['properties']['NUM SALES'] = salesTable.shape[0]

    # 6. Making Average Map   
    # Create Folium Map for averages and add TileLayer Options      
    m1 = folium.Map(location=city_cville_coord, zoom_start=13)
    folium.raster_layers.TileLayer('Open Street Map').add_to(m1)
    folium.raster_layers.TileLayer('Stamen Toner').add_to(m1)
    folium.raster_layers.TileLayer('CartoDB Positron').add_to(m1)

    # Group: GeoJson of Charlottesville City Boundary
    cityavg = salesTable.SaleAmount.mean()
    style_function = lambda x: {'fillColor': chooseColorSale(cityavg), 'color': chooseColorSale(cityavg)}
    city = folium.GeoJson(citylayer, name='City of Charlottesville Boundary',
                   style_function=style_function,
                         tooltip=folium.GeoJsonTooltip(['NAME','AVG PRICE','NUM SALES'])).add_to(m1)

    # Group: Addition of Markers for each individual Parcel
    saleMarkers = folium.FeatureGroup(name='Sale Price Markers',show=False)
    salesTable.apply(addMarkersSaleGroup,axis=1,args=(m1,saleMarkers))
    m1.add_child(saleMarkers)

    # Group: Choropleth of Planning Neighborhood Regions' Average Sale
    choroAvg = folium.Choropleth(geo_data=regionlayer, key_on='feature.properties.OBJECTID',
                      data=salesByRegionAvg,columns=['Regions','Avg'],
                      fill_color='YlOrRd',legend_name='Sale Amount ($): Average',name='Sale Amount Average', show=False,  
                      highlight=True).add_to(m1)
    choroAvg.geojson.add_child(folium.GeoJsonTooltip(['OBJECTID','NAME','AVG PRICE','NUM SALES']))
 

    # 7. Making Median Map      
    m2 = folium.Map(location=city_cville_coord, zoom_start=13)
    folium.raster_layers.TileLayer('Open Street Map').add_to(m2)
    folium.raster_layers.TileLayer('Stamen Toner').add_to(m2)
    folium.raster_layers.TileLayer('CartoDB Positron').add_to(m2)

    # 7. Group: GeoJson of Charlottesville City Boundary
    cityavg = salesTable.SaleAmount.mean()
    style_function = lambda x: {'fillColor': chooseColorSale(cityavg), 'color': chooseColorSale(cityavg)}
    city = folium.GeoJson(citylayer, name='City of Charlottesville Boundary',
                   style_function=style_function,
                         tooltip=folium.GeoJsonTooltip(['NAME','MED PRICE','NUM SALES'])).add_to(m2)

    # 8. Group: Addition of Markers for each individual Parcel
    saleMarkers = folium.FeatureGroup(name='Sale Price Markers',show=False)
    salesTable.apply(addMarkersSaleGroup,axis=1,args=(m2,saleMarkers))
    m2.add_child(saleMarkers)

    # 10. Group: Choropleth of Planning Neighborhood Regions' Median Sale
    choroMed = folium.Choropleth(geo_data=regionlayer, key_on='feature.properties.OBJECTID',
                      data=salesByRegionMed,columns=['Regions','Med'],
                      fill_color='YlOrRd',legend_name='Sale Amount ($): Median',name='Sale Amount Median', show=False,
                      highlight=True).add_to(m2)
    choroMed.geojson.add_child(folium.GeoJsonTooltip(['OBJECTID','NAME','MED PRICE','NUM SALES']))
    
    
    # 11. Add LayerControl and Save Map
    folium.LayerControl().add_to(m1)
    folium.LayerControl().add_to(m2)
    
    m1.save("figures/map-avg_sales.html")
    m2.save("figures/map-med_sales.html")
    return 

generateSalesMapSeparate()


def makeSalesTableByYear(t,y):
    start = str(y-1) + '-12-31'
    end = str(y+1) + '01-01'
    return t[(t.SaleDate > start) & (t.SaleDate < end)]


def plotyearlytrends():
    df = pd.read_csv('data/salesLast20Years.csv')
    df = df.drop(columns='Unnamed: 0')
    
    avgsale = []
    medsale = []
    years = np.arange(2000,2020)
    for i in years:
        temp = makeSalesTableByYear(df,i)
        avgsale.append(temp.SaleAmount.mean())
        medsale.append(temp.SaleAmount.median())
    
    df2 = pd.DataFrame([years,avgsale,medsale]).T
    df2.columns = ['Year','Average', 'Median']
    
    fig = ply.line(df2, x='Year',y=['Average','Median'])
    fig.update_layout(
    xaxis_title="Year",
    yaxis_title="Sale Amount ($)")
    fig.show()
    fig.write_html("figures/plot-avgmedsales.html")
    return 

plotyearlytrends()


def mean_med_byregion():
    avg_tab = pd.DataFrame(np.zeros((19,20)),index=np.arange(1,20),columns=[str(x) for x in np.arange(2000,2020)])
    med_tab = avg_tab.copy()
    table = pd.read_csv('data/salesLast20Years.csv')
    table = table.drop(columns='Unnamed: 0')
    for i in np.arange(2000,2020):
        temp = makeSalesTableByYear(table,i)
        avg_tab[str(i)] = temp.groupby(['Region']).mean().loc[:,'SaleAmount']
        med_tab[str(i)] = temp.groupby(['Region']).median().loc[:,'SaleAmount']
    
    avg_tab = avg_tab.fillna(0)
    med_tab = med_tab.fillna(0)
    
    return avg_tab,med_tab

avg_by_region, med_by_region = mean_med_byregion()
avg_by_region.T


def plotyearlyregionaltrends():
    avg, med = mean_med_byregion()
    
    avg = avg.T
    med = med.T
    
    avg["Year"] = avg.index
    med["Year"] = med.index

    fig = ply.line(avg,x='Year',y = list(range(1,20)),labels='Regions')
    fig.update_layout(
    xaxis_title="Year",
    yaxis_title="Average Sale Amount ($)")
    fig.show()
    fig.write_html("figures/plot-avg_sales_regional.html")
    
    fig = ply.line(med,x='Year',y = list(range(1,20)))
    fig.update_layout(
    xaxis_title="Year",
    yaxis_title="Median Sale Amount ($)")
    fig.show()
    fig.write_html("figures/plot-med_sales_regional.html")
    return 

plotyearlyregionaltrends()


def chooseColorTime(sale):
    if sale < 89000:
        return '#fef0d9'
    elif 89000<= sale < 150000:
        return '#fdd49e'
    elif 150000 <= sale < 300000:
        return '#fdbb84'
    elif 300000 <= sale < 500000:
        return '#fc8d59'
    elif 500000 <= sale < 800000:
        return '#e34a33'
    else:
        return '#b30000'

def makestyledict():
    datetime_index=pd.date_range(start='1/1/2000',end='1/1/2020',normalize=True,freq='A')
    dt_index_epochs=datetime_index.astype(int)//10**9
    dt_index=dt_index_epochs.astype(str)
    style = {
        str(outer_k): {
            inner_k: 0
            for inner_k in dt_index
        } 
        for outer_k in np.arange(1,20)
    }
    
    avg_by_region.columns=dt_index
    
    for region in style:
        for time in style[region]:
            saleavg = avg_by_region.at[int(region),str(time)]
            style[region][time] = {'color': chooseColorTime(saleavg) ,'opacity':1}
    return style


def generateTimesliderMap():
    r = gpd.read_file('data/regions.geojson').set_index('OBJECTID')
    m = folium.Map(location=city_cville_coord, zoom_start=13)
    g = plugins.TimeSliderChoropleth(r.to_json(), styledict=makestyledict(), overlay = True).add_to(m)
    m.save("figures/map-timeslider.html")
    return m

generateTimesliderMap()


def produceList(df):
    outer = []
    for year in np.arange(2000,2020):
        inner = []
        temp = df[df['SaleDate'].dt.year==year].copy()
        for house in temp.iterrows():
            innerer = []
            innerer.append(house[1].LATITUDE)
            innerer.append(house[1].LONGITUDE)
            innerer.append(house[1].SaleAmountScaled)
            inner.append(innerer)
        outer.append(inner)
    return outer


def generateHeatMapSales():
    df=pd.read_csv('data/salesLast20Years.csv')
    df=df.drop(columns='Unnamed: 0')
    df['SaleDate'] = pd.DatetimeIndex(df['SaleDate'])

    dfLog = df.copy()
    
    scaler = MinMaxScaler(feature_range=(0.0001,1))
    df['SaleAmountScaled'] = scaler.fit_transform(df.loc[:,'SaleAmount'].values.reshape(-1,1))
    
    scalerLog = MinMaxScaler(feature_range=(0.0001,1))
    dfLog['SaleAmountScaled'] = scaler.fit_transform(np.log(df.loc[:,'SaleAmount'].values.reshape(-1,1)))
    
    m = folium.Map(location=city_cville_coord, zoom_start=13)
    data = produceList(df)
    dataLog = produceList(dfLog)
    
    yearsnorm = ['Norm: '+str(i) for i in np.arange(2000,2020)]
    yearslog = ['Log: '+str(i) for i in np.arange(2000,2020)]

    g = plugins.HeatMapWithTime(data=data,index=yearsnorm,name='Normal',
                                use_local_extrema=True, 
                                min_speed = 1, speed_step = 1, max_speed = 5).add_to(m)
    g = plugins.HeatMapWithTime(data = dataLog, index = yearslog, name = 'Log',
                                use_local_extrema=True, 
                                min_speed = 1, speed_step = 1, max_speed = 5, show=False).add_to(m)
    
    style_function = lambda x: {'fillOpacity': 0, 'opacity': 0.5}

    folium.GeoJson("data/regions.geojson", style_function=style_function,name='Neighborhood Planning Areas').add_to(m)

    folium.raster_layers.TileLayer('CartoDB Positron',show=True).add_to(m)
    folium.raster_layers.TileLayer('Open Street Map',show=False).add_to(m)
    folium.raster_layers.TileLayer('Stamen Toner',show=False).add_to(m)
    folium.LayerControl().add_to(m)
    
    m.save("figures/map-heatmap.html")
    return m

generateHeatMapSales()


grpbysale = geoSalesResid.groupby(['ParcelNumber']).count().iloc[:,0]

multisales = grpbysale[grpbysale > 1]
print('Total sales: ' + str(geoSalesResid.shape[0]))
print('Unique parcels: ' + str(grpbysale.shape[0]))
print('Unique parcels with two or more sales: ' + str(multisales.shape[0]))

Total sales: 30618
Unique parcels: 12309
Unique parcels with two or more sales: 9107


def normal(parcel):
    temp = {}
    if parcel.shape[0] == 1:
        saledate = parcel.iloc[0,2]
        temp[saledate] = parcel.iloc[0,3]
    else:
        for i in np.arange(parcel.shape[0]):
            saledate = parcel.iloc[i,2]
            temp[saledate] = parcel.iloc[i,3]
    return temp

def byYear(parcel,year=2000):
    temp = {}
    if parcel.shape[0] == 1:
        saledate = parcel.iloc[0,2]
        if int(saledate[:4]) >= year:
            temp[saledate] = parcel.iloc[0,3]
    else:
        for i in np.arange(parcel.shape[0]):
            saledate = parcel.iloc[i,2]           
            if int(saledate[:4]) >= year:
                temp[saledate] = parcel.iloc[i,3]
    return temp


def getSalesHistory(table, norm=True, multi=True):
    grouped = table.groupby('ParcelNumber')
    if norm:
        history = grouped.apply(normal).to_dict()
    else:
        temp = grouped.apply(byYear).to_dict()
        history = {}
        for parcel in temp:
            if len(temp[parcel]) > 1:
                history[parcel] = temp[parcel]
            elif not multi and len(temp[parcel]) == 1:
                history[parcel] = temp[parcel]
    return history


saleHistory = getSalesHistory(geoSalesResid,norm=False)
saleHistory

{'010017000': {'2017-06-08': 636000, '2018-10-19': 1030000},
 '010017100': {'2000-09-14': 459500, '2005-11-10': 654750},
 '010019000': {'2002-05-23': 85000,
  '2006-07-07': 479500,
  '2003-03-03': 360000,
  '2001-11-01': 70000,
  '2008-08-07': 549000},
 '010020000': {'2010-08-04': 962500, '2001-01-24': 275000},
 '010024A00': {'2002-06-12': 125000,
  '2014-06-30': 940000,
  '2007-08-29': 895000},
 '010027000': {'2008-08-04': 483000, '2017-12-08': 695000},
 '010031000': {'2003-06-06': 425000, '2008-12-15': 545000},
 '010034000': {'2018-11-15': 430000, '2019-02-15': 470000},
 '010036000': {'2014-03-21': 695940, '2010-11-01': 389000},
 '010037000': {'2016-05-05': 740000, '2001-07-02': 345000},
 '010038000': {'2018-04-09': 685000, '2019-03-29': 1375000},
 '010039000': {'2007-07-13': 520000, '2007-05-25': 520000},
 '010041000': {'2004-03-03': 530000, '2019-06-06': 1350000},
 '010043000': {'2020-01-07': 680000,
  '2018-07-17': 660000,
  '2011-05-25': 315000,
  '2015-05-06': 590000},
 '010045000': {'2008-07-30': 745000, '2006-11-29': 725000},
 '020003000': {'2007-08-10': 948000, '2003-06-03': 500000},
 '020004000': {'2005-05-06': 377500, '2004-02-11': 275000},
 '020006000': {'2009-04-23': 600000, '2003-11-18': 310000},
 '020006100': {'2016-10-05': 355000,
  '2007-04-17': 362500,
  '2004-05-03': 300000},
 '020006300': {'2012-11-29': 386375, '2015-06-30': 412500},
 '020010000': {'2005-03-02': 430000,
  '2016-12-20': 155000,
  '2019-08-19': 373450},
 '020012000': {'2005-12-14': 350000,
  '2006-06-12': 350000,
  '2016-07-28': 379000},
 '020015000': {'2019-01-25': 252500, '2019-10-17': 550000},
 '020016100': {'2010-06-03': 350000, '2016-06-30': 478000},
 '020016200': {'2015-01-28': 415000, '2013-10-16': 83000},
 '020017000': {'2000-02-29': 208000, '2001-08-31': 339000},
 '020019000': {'2020-05-13': 1975000, '2003-06-13': 810000},
 '020019100': {'2011-12-01': 640000, '2018-08-10': 767500},
 '020023A00': {'2002-06-10': 2967,
  '2016-10-07': 851000,
  '2017-02-23': 848000,
  '2013-02-12': 650000},
 '020025000': {'2010-09-02': 1100000, '2015-07-27': 735000},
 '020028000': {'2010-12-29': 675000, '2002-07-23': 385000},
 '020029000': {'2002-03-04': 425000,
  '2002-05-28': 456000,
  '2015-06-12': 776795},
 '020042000': {'2018-04-02': 593800,
  '2010-07-09': 550000,
  '2020-04-24': 814000},
 '020044000': {'2008-06-16': 425000, '2006-05-01': 408400},
 '020046000': {'2002-08-09': 402000, '2000-12-11': 264000},
 '020051000': {'2015-04-21': 515000, '2011-01-18': 418000},
 '020054200': {'2010-07-13': 650000, '2003-10-31': 525000},
 '020054500': {'2004-08-23': 20963,
  '2013-05-24': 960000,
  '2009-05-14': 210000},
 '020055200': {'2011-06-27': 500000, '2013-09-06': 750000},
 '020055500': {'2011-04-22': 560000, '2017-08-04': 631250},
 '020055600': {'2003-06-16': 405000, '2000-08-02': 265400},
 '020057000': {'2007-06-01': 395000,
  '2018-06-08': 785000,
  '2013-09-09': 720000,
  '2002-07-16': 429000},
 '020058000': {'2008-02-01': 191350, '2015-03-24': 375000},
 '020061000': {'2015-01-20': 850000,
  '2000-04-19': 335000,
  '2012-07-23': 753000},
 '020062000': {'2007-02-27': 515000, '2014-11-12': 549000},
 '020066000': {'2015-06-09': 495000, '2002-11-15': 309000},
 '020070000': {'2008-11-21': 44900, '2004-08-20': 1100000},
 '020072000': {'2016-02-02': 695600, '2020-04-29': 990000},
 '030001000': {'2010-09-15': 465000, '2012-06-22': 480000},
 '030004000': {'2002-12-10': 213000,
  '2019-03-05': 735000,
  '2008-07-03': 435500,
  '2000-04-28': 153000},
 '030010000': {'2013-07-10': 450000,
  '2009-10-28': 399900,
  '2018-06-05': 595000,
  '2007-08-13': 370000},
 '030015000': {'2005-07-20': 380000,
  '2013-05-29': 427000,
  '2016-08-17': 460000,
  '2003-09-08': 350000,
  '2001-02-07': 225000},
 '030016000': {'2005-07-27': 425000, '2015-07-09': 739000},
 '030023000': {'2003-03-24': 258000,
  '2017-12-01': 520000,
  '2001-08-07': 175000},
 '030042000': {'2016-08-26': 459000,
  '2007-06-29': 420000,
  '2003-07-22': 266000},
 '030043000': {'2019-07-02': 653000, '2001-07-02': 245000},
 '030045000': {'2006-02-28': 799000, '2017-06-01': 769000},
 '030045100': {'2004-09-15': 238000, '2009-12-17': 285000},
 '030046000': {'2012-04-30': 500000, '2003-07-02': 455000},
 '030048000': {'2004-06-07': 309000, '2013-06-04': 341000},
 '030050000': {'2005-07-28': 380000, '2014-05-27': 349900},
 '030051000': {'2014-02-21': 417438,
  '2018-06-19': 465000,
  '2015-02-06': 290000,
  '2006-08-02': 379500},
 '030052000': {'2004-07-27': 540000, '2016-12-09': 630000},
 '030052100': {'2002-08-15': 325000, '2006-05-05': 390000},
 '030059000': {'2008-07-23': 122744,
  '2004-01-09': 160000,
  '2008-08-15': 126000},
 '030060100': {'2001-03-13': 246000, '2005-05-24': 375000},
 '030060101': {'2005-06-24': 410000, '2011-08-05': 340000},
 '030060120': {'2010-04-15': 620000, '2001-03-23': 50000},
 '030060130': {'2018-07-26': 543510, '2000-01-14': 211963},
 '030060140': {'2002-02-13': 10000, '2007-05-25': 585000},
 '030060160': {'2000-11-17': 276935, '2011-06-13': 498000},
 '030060170': {'2013-04-11': 449000,
  '2008-05-01': 447500,
  '2000-07-13': 244900},
 '030060200': {'2004-07-16': 370000,
  '2000-09-19': 240000,
  '2013-08-16': 401000},
 '030060300': {'2003-05-29': 302000, '2019-10-10': 503000},
 '030060400': {'2000-07-12': 208825, '2009-09-01': 415500},
 '030060600': {'2005-03-02': 620000,
  '2004-01-07': 190000,
  '2015-05-20': 684500},
 '030060700': {'2004-01-07': 190000,
  '2018-05-01': 625000,
  '2006-06-01': 807794},
 '030060800': {'2001-06-13': 283500, '2004-06-22': 365000},
 '030060900': {'2005-10-18': 412000,
  '2002-07-31': 295000,
  '2012-09-12': 330000},
 '030065000': {'2005-07-27': 10000, '2004-08-03': 310000},
 '030067000': {'2004-10-27': 38000, '2002-03-22': 140000},
 '030067100': {'2002-06-25': 193000,
  '2003-06-04': 215000,
  '2008-06-17': 349000},
 '030073000': {'2005-10-20': 327500, '2001-04-06': 126000},
 '030076000': {'2000-12-08': 30000, '2006-10-23': 375000},
 '030077000': {'2006-10-23': 375000,
  '2004-04-29': 65000,
  '2001-05-14': 36000,
  '2004-10-05': 91500},
 '030079000': {'2014-12-19': 125000, '2015-07-13': 448000},
 '030080101': {'2005-03-24': 320000, '2004-08-12': 289900},
 '030080200': {'2003-07-01': 385000, '2005-08-25': 428000},
 '030080300': {'2005-07-22': 325000, '2020-05-01': 445000},
 '030080400': {'2015-08-28': 385000,
  '2003-07-18': 291000,
  '2013-01-23': 375000},
 '030080600': {'2007-07-31': 415000,
  '2000-07-24': 178000,
  '2003-09-16': 266000},
 '030080700': {'2003-08-26': 278000,
  '2016-03-04': 315000,
  '2006-04-04': 349000},
 '030080800': {'2001-06-28': 205000,
  '2005-02-10': 350000,
  '2016-06-09': 421500},
 '030089000': {'2005-06-16': 325000, '2020-03-05': 375000},
 '030090000': {'2010-11-23': 150401,
  '2011-03-08': 163000,
  '2006-07-12': 155000},
 '030097000': {'2002-12-17': 166500,
  '2002-05-15': 107000,
  '2019-12-11': 275000},
 '030098000': {'2000-06-20': 15430, '2003-09-05': 62000},
 '030100000': {'2002-10-16': 5772, '2002-02-20': 4160},
 '030108000': {'2004-04-01': 90000, '2000-12-07': 53000},
 '030109000': {'2006-05-25': 180000,
  '2015-03-09': 220000,
  '2002-09-18': 70000,
  '2020-05-07': 304000,
  '2004-05-11': 119500},
 '030110000': {'2005-07-27': 175000, '2010-09-16': 135000},
 '030113000': {'2017-10-31': 195000,
  '2012-08-07': 125000,
  '2012-11-30': 141900},
 '030140000': {'2006-06-06': 245000, '2001-08-02': 128000},
 '030142000': {'2002-05-02': 140000,
  '2007-05-02': 250000,
  '2018-07-23': 231000},
 '040008000': {'2017-10-11': 215000,
  '2007-07-12': 173000,
  '2006-06-22': 7795,
  '2006-04-14': 130000,
  '2002-06-04': 64000},
 '040020000': {'2008-05-23': 215000,
  '2012-03-12': 195000,
  '2007-09-13': 132282},
 '040033000': {'2009-07-07': 71000, '2005-04-04': 2500, '2009-02-20': 159814},
 '040047000': {'2006-07-11': 85000, '2007-06-19': 355000},
 '040067000': {'2014-05-23': 386000, '2013-07-17': 63000},
 '040070000': {'2004-08-09': 245000, '2002-05-09': 70000},
 '040076000': {'2015-02-18': 50000,
  '2012-03-08': 140000,
  '2016-11-07': 170000},
 '040083000': {'2019-10-02': 502000,
  '2016-10-31': 400000,
  '2016-03-29': 179845},
 '040086000': {'2007-01-23': 245000, '2017-01-10': 265500},
 '040099000': {'2015-03-23': 125000,
  '2018-12-17': 235000,
  '2020-02-28': 416000},
 '040100000': {'2013-12-19': 231000,
  '2012-07-28': 398803,
  '2013-07-03': 398809,
  '2004-02-25': 40800,
  '2004-12-06': 171940},
 '040110000': {'2000-11-17': 19000, '2013-10-15': 57000},
 '040136000': {'2000-02-18': 85000, '2017-05-18': 265000},
 '040137000': {'2008-07-02': 180000, '2015-11-16': 222000},
 '040137100': {'2019-08-14': 280000,
  '2015-08-10': 240000,
  '2008-06-26': 180000},
 '040138000': {'2005-10-27': 238200, '2002-11-18': 70000, '2004-02-03': 87000},
 '040140000': {'2001-02-26': 61000, '2000-11-17': 56000},
 '040147000': {'2005-06-16': 220000,
  '2004-06-07': 11000,
  '2008-07-28': 286000},
 '040151000': {'2012-05-25': 125000, '2015-11-13': 176750},
 '040169000': {'2003-02-11': 35000,
  '2002-10-16': 600,
  '2002-03-11': 7000,
  '2004-10-04': 145750},
 '040171000': {'2004-01-20': 120000, '2005-12-30': 172500},
 '040174000': {'2005-01-06': 105000, '2005-07-13': 174900},
 '040181000': {'2017-05-30': 172500,
  '2008-07-02': 139500,
  '2007-10-11': 90000},
 '040182000': {'2012-06-18': 114750,
  '2013-01-17': 111100,
  '2003-06-19': 55000},
 '040184000': {'2016-07-06': 330900,
  '2013-03-13': 144000,
  '2013-07-31': 66299,
  '2003-06-19': 45000},
 '040194000': {'2001-02-13': 17000, '2002-01-31': 79000},
 '040195000': {'2017-01-17': 90000, '2005-01-12': 46533},
 '040197000': {'2005-02-07': 155000,
  '2004-01-06': 39359,
  '2004-03-30': 58500,
  '2004-10-12': 90000,
  '2017-08-03': 248000},
 '040205000': {'2002-02-01': 100000, '2000-10-24': 45000},
 '040211000': {'2014-06-12': 215000, '2008-09-05': 150000},
 '040212000': {'2008-09-04': 122500,
  '2013-03-29': 194000,
  '2016-11-22': 289000},
 '040213000': {'2018-11-26': 311600, '2018-05-16': 151000},
 '040219000': {'2015-12-17': 117000,
  '2017-09-20': 215000,
  '2019-03-22': 254939},
 '040221000': {'2000-02-29': 45000,
  '2007-04-02': 121025,
  '2016-04-11': 250000},
 '040225000': {'2020-01-22': 330000, '2006-04-25': 219900, '2000-05-26': 5500},
 '040227000': {'2007-01-04': 250000, '2001-01-19': 286400},
 '040228000': {'2000-08-14': 55750, '2006-06-19': 345000},
 '040229000': {'2002-01-31': 30500,
  '2006-04-20': 305000,
  '2008-07-16': 299000,
  '2019-11-12': 369000},
 '040231000': {'2001-01-19': 286400,
  '2017-06-01': 365000,
  '2006-03-20': 300000},
 '040232000': {'2005-12-08': 299900,
  '2013-05-29': 286000,
  '2001-01-19': 286400},
 '040242000': {'2011-12-13': 70000, '2011-04-08': 63000, '2011-03-17': 100643},
 '040242100': {'2003-06-30': 25000, '2005-09-13': 152000},
 '040265000': {'2005-02-23': 184000,
  '2008-05-27': 252500,
  '2001-01-19': 286400,
  '2016-06-07': 315000},
 '040269000': {'2005-08-29': 225000,
  '2003-06-26': 51000,
  '2009-12-29': 185000},
 '040270000': {'2016-12-19': 188500,
  '2017-04-26': 335000,
  '2001-01-19': 286400,
  '2003-09-02': 125000},
 '040271000': {'2003-07-30': 127950,
  '2013-12-09': 225000,
  '2010-10-18': 180000,
  '2001-01-19': 286400},
 '0402771D0': {'2006-01-12': 228000,
  '2012-10-10': 226000,
  '2004-12-23': 193500},
 '0402772B0': {'2017-04-24': 285000, '2009-07-16': 222500},
 '0402772G0': {'2009-07-28': 222500, '2017-05-08': 282000},
 '040280100': {'2004-02-06': 775000,
  '2001-12-05': 38000,
  '2007-04-20': 2400000},
 '040280101': {'2012-06-29': 145600,
  '2005-02-25': 115000,
  '2004-11-01': 81500},
 '040280120': {'2002-08-29': 62000, '2007-04-20': 2400000},
 '040280140': {'2004-06-15': 78000,
  '2005-03-01': 131100,
  '2009-08-05': 140000},
 '040280150': {'2007-04-20': 2400000,
  '2003-10-31': 57000,
  '2004-02-06': 775000},
 '040280160': {'2002-05-16': 44500,
  '2007-04-20': 2400000,
  '2004-02-06': 775000},
 '040280170': {'2007-03-27': 156500,
  '2015-07-31': 156000,
  '2003-06-05': 88500,
  '2002-05-31': 62500},
 '040280190': {'2002-01-28': 47000,
  '2007-04-20': 2400000,
  '2004-02-06': 775000},
 '040280200': {'2004-02-06': 775000,
  '2002-02-01': 44500,
  '2007-04-20': 2400000},
 '040280210': {'2005-01-19': 77000,
  '2007-04-20': 2400000,
  '2001-02-23': 44000},
 '040280220': {'2004-01-20': 88000, '2010-04-23': 153000},
 '040280230': {'2007-07-02': 155000,
  '2004-02-06': 92000,
  '2017-03-31': 178000,
  '2002-09-19': 53000},
 '040280240': {'2002-06-03': 49900,
  '2007-04-20': 2400000,
  '2004-02-06': 775000},
 '040280250': {'2004-02-06': 775000,
  '2007-04-20': 2400000,
  '2002-02-28': 31500},
 '040280260': {'2002-08-29': 248000, '2007-04-20': 2400000},
 '040280290': {'2004-02-06': 775000,
  '2003-10-31': 55000,
  '2007-04-20': 2400000},
 '040280300': {'2002-08-29': 248000, '2007-04-20': 2400000},
 '040280301': {'2007-04-20': 2400000, '2002-08-29': 248000},
 '040280310': {'2002-06-03': 49000,
  '2004-02-06': 775000,
  '2007-04-20': 2400000},
 '040280330': {'2007-04-20': 2400000,
  '2003-10-14': 45000,
  '2004-02-06': 775000},
 '040280340': {'2007-04-20': 2400000, '2002-08-29': 248000},
 '040280360': {'2011-09-01': 140000, '2002-12-12': 63000},
 '040280400': {'2002-05-16': 31000,
  '2004-02-06': 775000,
  '2007-04-20': 2400000},
 '040280600': {'2007-04-20': 2400000,
  '2004-02-06': 775000,
  '2002-09-17': 51000},
 '040280900': {'2005-06-01': 128000,
  '2003-04-15': 85500,
  '2014-07-31': 144500},
 '040303101': {'2012-04-25': 125200, '2015-07-17': 138333},
 '040303110': {'2015-04-03': 99000, '2003-05-30': 76500, '2011-09-06': 110000},
 '040303120': {'2007-04-03': 160000,
  '2001-06-04': 52500,
  '2018-03-15': 530000},
 '040303130': {'2003-03-11': 70000,
  '2018-03-15': 530000,
  '2007-04-03': 160000},
 '040303170': {'2005-01-18': 137500, '2015-01-30': 165000},
 '040303180': {'2019-05-22': 175000,
  '2015-09-10': 140000,
  '2014-09-23': 267000},
 '040303190': {'2003-10-30': 107000, '2012-03-07': 122000},
 '040303310': {'2004-12-13': 118000,
  '2003-06-26': 104000,
  '2010-09-02': 122000},
 '040303340': {'2012-08-30': 122600,
  '2015-07-17': 138333,
  '2000-03-10': 50000},
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  '2000-12-11': 105900},
 '200265000': {'2003-03-28': 101000,
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 '200266200': {'2018-04-30': 326820, '2017-12-27': 492679},
 '200266300': {'2018-02-16': 297591, '2017-12-27': 492679},
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 '200268300': {'2017-08-17': 1112021, '2017-08-31': 274900},
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 '200269300': {'2018-01-18': 288641, '2017-08-17': 1112021},
 '200269400': {'2017-08-17': 1112021, '2017-12-21': 299900},
 '200270100': {'2017-09-20': 562440, '2017-09-25': 303822},
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 '210007000': {'2002-07-30': 126500,
  '2005-08-09': 215000,
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 '210008000': {'2006-12-22': 280000,
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 '210020000': {'2017-06-09': 241900, '2005-07-29': 205000},
 '210025100': {'2012-05-22': 206000, '2005-07-21': 197000},
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 '210033000': {'2013-11-21': 326000,
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 '210100000': {'2017-07-05': 259500,
  '2007-12-04': 235000,
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 '210101000': {'2017-12-21': 500000, '2013-07-17': 314000},
 '210102000': {'2007-02-26': 280000, '2017-12-27': 380500},
 '210102100': {'2019-09-03': 519000,
  '2012-12-27': 435000,
  '2006-03-31': 125000,
  '2009-12-04': 65000,
  '2009-01-21': 125000,
  '2005-08-12': 112000},
 '210104000': {'2008-06-16': 285000,
  '2007-10-05': 250000,
  '2014-01-16': 230000},
 '210104400': {'2003-07-15': 165000,
  '2000-06-19': 110000,
  '2007-03-28': 250000},
 '210105100': {'2014-12-19': 210000, '2018-08-30': 315000},
 '210105101': {'2013-05-31': 235000,
  '2005-03-21': 250000,
  '2000-07-18': 110000},
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 '210105260': {'2003-06-13': 226000,
  '2012-07-02': 266250,
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 '210105400': {'2012-11-19': 160000, '2002-07-11': 150000},
 '210106000': {'2013-10-31': 255000, '2014-11-04': 263000},
 '210107000': {'2008-05-01': 220000,
  '2019-04-03': 270000,
  '2004-07-13': 200000},
 '210120000': {'2010-05-20': 376000, '2002-04-02': 136900},
 '210123000': {'2002-10-16': 222000, '2000-09-25': 142500},
 '210125100': {'2005-07-27': 309000,
  '2015-06-04': 345000,
  '2002-01-31': 165000,
  '2009-08-11': 335000},
 '210126000': {'2018-09-26': 442000, '2003-08-13': 298200},
 '210129000': {'2016-03-21': 173775, '2015-07-14': 483881},
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 '210131300': {'2012-07-26': 356000,
  '2010-03-31': 345000,
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 '21A001100': {'2017-10-02': 166500, '2017-03-03': 119500},
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 '21A003000': {'2004-05-17': 210000, '2011-07-19': 206500},
 '21A004000': {'2004-05-04': 186000,
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 '21A009000': {'2007-11-30': 220000,
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 '21A012000': {'2003-09-03': 164300, '2009-09-24': 245000},
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 '21A019000': {'2014-06-17': 239000,
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 '21A020000': {'2002-06-28': 185000, '2003-07-01': 211000},
 '21A021000': {'2004-09-16': 95000, '2000-04-28': 115000},
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 '21A023000': {'2018-06-18': 377000, '2012-11-14': 166500},
 '21A027000': {'2019-06-18': 302900, '2018-11-26': 175000},
 '21A028000': {'2004-08-17': 240000,
  '2020-05-01': 325200,
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 '21A031000': {'2008-08-01': 252500,
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 '21A035000': {'2004-11-04': 200500,
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 '21A037000': {'2009-07-09': 239000,
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  '2003-06-04': 175000},
 '21A038000': {'2007-05-22': 235000, '2017-04-25': 275000},
 '21A039000': {'2006-07-21': 224300,
  '2010-09-16': 175521,
  '2018-03-29': 375000},
 '21A042000': {'2009-07-06': 257000, '2002-07-16': 171000},
 '21A048000': {'2014-05-22': 244000,
  '2009-05-29': 229500,
  '2018-05-11': 327000,
  '2002-10-25': 180000},
 '21A055000': {'2001-04-03': 150000, '2007-04-16': 168750},
 '21A057000': {'2014-03-26': 249750, '2002-06-20': 169800},
 '21A059000': {'2009-03-24': 210000, '2013-06-18': 248000},
 '21A064000': {'2014-03-07': 227500, '2016-05-16': 280000},
 '21A068000': {'2002-05-31': 160000,
  '2018-01-26': 341000,
  '2010-08-31': 240500,
  '2015-10-27': 294000},
 '21A069000': {'2018-05-31': 375000,
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  '2009-08-18': 180368,
  '2010-03-30': 135900,
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 '21A070000': {'2015-11-23': 317500,
  '2010-04-08': 255000,
  '2014-08-13': 281200},
 '21A071000': {'2019-05-13': 250000, '2020-04-15': 322000},
 '21A073000': {'2015-09-21': 219000, '2011-07-07': 205000},
 '21A076000': {'2005-05-24': 247000, '2007-11-28': 225000},
 '21A085000': {'2001-11-19': 182500, '2003-08-20': 190000},
 '21A087000': {'2001-12-06': 138500,
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 '21A087100': {'2007-05-01': 107500,
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 '21A090000': {'2013-06-04': 425000,
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 '21A090100': {'2012-11-15': 450000,
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 '21A090200': {'2013-08-02': 376937, '2012-11-15': 450000},
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 '21A090400': {'2012-11-15': 450000, '2013-12-17': 465000},
 '21A090500': {'2013-12-31': 450000,
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 '21A094000': {'2019-01-25': 282500, '2018-09-13': 245000},
 '21A095000': {'2004-01-30': 127000,
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  '2013-11-18': 220000,
  '2004-11-18': 224000},
 '21A095A00': {'2013-07-18': 355000, '2004-02-17': 39500},
 '21A098000': {'2017-12-08': 220000, '2018-08-21': 293000},
 '21A099100': {'2013-06-12': 273000,
  '2017-06-12': 314900,
  '2009-12-18': 240000},
 '21A099130': {'2004-05-12': 192500,
  '2010-10-15': 219000,
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 '21A099180': {'2019-03-27': 203000,
  '2002-04-02': 99500,
  '2002-12-16': 134000},
 '21A099210': {'2000-06-13': 93500, '2014-08-21': 141000},
 '21A099220': {'2018-08-13': 210000, '2005-02-01': 120000},
 ...}


def strictincr(saleHistory):
    strictinc = {}
    for parcel in saleHistory:
        temp = pd.DataFrame.from_dict(saleHistory[parcel],orient='index')
        temp = temp.sort_index(ascending=True)
        curr = 0
        datesOrd = temp.index.tolist()
        strictinc[parcel] = True
        for saleday in datesOrd:
            if curr < saleHistory[parcel][saleday]:
                curr =  saleHistory[parcel][saleday]
            else:
                strictinc[parcel] = False
                break
    return strictinc

def finalincr(saleHistory):
    finalinc = {}
    for parcel in saleHistory:
        temp = pd.DataFrame.from_dict(saleHistory[parcel],orient='index')
        temp = temp.sort_index(ascending=True)
        
        datesOrd = temp.index.tolist()
        finalinc[parcel]= True
        firstsale = saleHistory[parcel][datesOrd[0]]
        lastsale = saleHistory[parcel][datesOrd[len(datesOrd)-1]]
        if firstsale > lastsale:
            finalinc[parcel] = False
    return finalinc


strictTab = pd.DataFrame.from_dict(strictincr(saleHistory),orient='index',columns=['strictincr'])
finalTab = pd.DataFrame.from_dict(finalincr(saleHistory),orient='index',columns=['finalincr'])


print("Proportion of Parcels that increased in price every subsequent sale: "+ str(round(strictTab.sum()[0]/len(saleHistory),5)))
print("Proportion of Parcels that increased in price from first to last sale: "+ str(round(finalTab.sum()[0]/len(saleHistory),5)))

Proportion of Parcels that increased in price every subsequent sale: 0.62706
Proportion of Parcels that increased in price from first to last sale: 0.78668


def incrPercent(row,saleHistory):
    temp = pd.DataFrame.from_dict(saleHistory[row.name],orient='index')
    temp = temp.sort_index(ascending=True)
    datesOrd = temp.index.tolist()
    firstsale = saleHistory[row.name][datesOrd[0]]
    lastsale = saleHistory[row.name][datesOrd[len(datesOrd)-1]]
    val = (lastsale-firstsale)/firstsale * 100
    return val

def incrPercentDay(row,saleHistory):
    temp = pd.DataFrame.from_dict(saleHistory[row.name],orient='index')
    temp = temp.sort_index(ascending=True)
    datesOrd = temp.index.tolist()
    
    firstdate = datetime.datetime.strptime(datesOrd[0], "%Y-%m-%d").date()
    lastdate = datetime.datetime.strptime(datesOrd[len(datesOrd)-1], "%Y-%m-%d").date()
    
    diff = (lastdate-firstdate).days
    firstsale = saleHistory[row.name][datesOrd[0]]
    lastsale = saleHistory[row.name][datesOrd[len(datesOrd)-1]]      
    return (lastsale-firstsale)/firstsale * 100/diff


percentTab = pd.DataFrame(finalTab[finalTab.finalincr])
percentTab.insert(percentTab.shape[1],'perc',finalTab[finalTab.finalincr].apply(incrPercent,axis=1,args=(saleHistory,)))
percentTab.insert(percentTab.shape[1],'percNorm',finalTab[finalTab.finalincr].apply(incrPercentDay,axis=1,args=(saleHistory,)))
geoSalesResidPercent = pd.merge(makeRecentSales('1999-12-31'),percentTab,left_on='ParcelNumber', right_index=True).drop_duplicates('ParcelNumber')
geoSalesResidPercent


def chooseColorPerc(sale):
    if sale < 5:
        return '#fef0d9'
    elif 5 <= sale < 10:
        return '#fdd49e'
    elif 20 <= sale < 50:
        return '#fdbb84'
    elif 50 <= sale < 100:
        return '#fc8d59'
    elif 100 <= sale < 200:
        return '#e34a33'
    else:
        return '#b30000'
    
    
def chooseColorPercNorm(sale):
    if sale < 0.01:
        return '#fef0d9'
    elif 0.01 <= sale < 0.05:
        return '#fdd49e'
    elif 0.05 <= sale < 0.10:
        return '#fdbb84'
    elif 0.10 <= sale < 0.25:
        return '#fc8d59'
    elif 0.25 <= sale < 0.5:
        return '#e34a33'
    else:
        return '#b30000'
    

def addMarkersPerc(row,currmap,norm,group):
    # not normalized to day (perc)
    if not norm:
        line = str(row['perc']) + '%'
        marker = folium.CircleMarker(location=[row['LATITUDE'],row['LONGITUDE']],
                      popup=line,radius=1.5,color=chooseColorPerc(row['perc']),
                           fill_color=chooseColorPerc(row['perc']))
    # normalized to day (percNorm)
    else:
        line = str(row['percNorm']) + '%'
        marker = folium.CircleMarker(location=[row['LATITUDE'],row['LONGITUDE']],
              popup=line,radius=1.5,color=chooseColorPercNorm(row['percNorm']),
                   fill_color=chooseColorPercNorm(row['percNorm']))
    group.add_child(marker)
    return


def generateMapPercent(table):
    # 1. Import the DataFrame that classifies each house to a region
    classifiedregions = pd.read_csv('data/salesLast20Years.csv') 
    classifiedregions = classifiedregions.loc[:,['ParcelNumber','Region']]
    classifiedregions = classifiedregions.drop_duplicates(subset='ParcelNumber')
    
    # 2. Merge with the one that has the percent change in sales
    table = table.merge(classifiedregions,left_on='ParcelNumber',right_on='ParcelNumber')
    
    # 3. Group by region number and find average sale amount and median in each, even for region 0, then make a DataFrame
    # with region numbers as a column
    
    percByRegionAvg = produceAGroup(table,'mean','perc')
    percByRegionMed = produceAGroup(table,'median','perc')
    percNormByRegionAvg = produceAGroup(table,'mean','percNorm')
    percNormByRegionMed = produceAGroup(table,'median','percNorm')
    countByRegion = produceAGroup(table,'count','-')

    # 4. load GeoJSON file of regions, adding the SaleAverage as a property to each
    with open ("data/regions.geojson") as f:
        regionlayer = json.load(f)
        count = 1
        for i in regionlayer['features']:
            i['properties']['% DIFF AVG'] = checkIfZero(percByRegionAvg, 'Avg',count)
            i['properties']['% DIFF MED'] = checkIfZero(percByRegionMed, 'Med',count)
            i['properties']['% NORM DIFF AVG'] = checkIfZero(percNormByRegionAvg, 'Avg',count) 
            i['properties']['% NORM DIFF MED'] = checkIfZero(percNormByRegionMed, 'Med',count) 
            i['properties']['NUM PARCELS'] = checkIfZero(countByRegion, 'Count',count) 
            count += 1
            
    # 5. Load GeoJSON of city boundary, adding SaleAverage/Median of whole city to the Layer
    with open ("data/cityboundary.geojson") as f:
        citylayer = json.load(f)
    citylayer['features'][0]['properties']['% DIFF AVG'] = str(round(table.perc.mean(),2)) + '%'
    citylayer['features'][0]['properties']['% DIFF MED'] = str(round(table.perc.median(),2)) + '%'
    citylayer['features'][0]['properties']['% NORM DIFF AVG'] = str(round(table.percNorm.mean(),2)) + '%'
    citylayer['features'][0]['properties']['% NORM DIFF MED'] = str(round(table.percNorm.median(),2)) + '%'
    citylayer['features'][0]['properties']['NUM PARCELS'] = str(table.perc.mean()) + '%'


    # 6. Create Folium Map and add TileLayer Options      
    m = plugins.DualMap(location=city_cville_coord, zoom_start=13)
    folium.raster_layers.TileLayer('Open Street Map').add_to(m)
    folium.raster_layers.TileLayer('Stamen Toner').add_to(m)
    folium.raster_layers.TileLayer('CartoDB Positron').add_to(m)
    
    # 7. Group: GeoJson of Charlottesville City Boundary    
    cityavg = table.perc.mean()
    style_function1 = lambda x: {'fillColor': chooseColorPerc(cityavg), 'color': chooseColorPerc(cityavg)}
    city1 = folium.GeoJson(citylayer, name='City of Charlottesville Boundary',
                   style_function=style_function1,
                         tooltip=folium.GeoJsonTooltip(['NAME','% DIFF AVG','% DIFF MED'])).add_to(m.m1)
    cityavgnorm = table.percNorm.mean()
    style_function2 = lambda x: {'fillColor': chooseColorPercNorm(cityavgnorm), 'color': chooseColorPercNorm(cityavgnorm)}
    city2 = folium.GeoJson(citylayer, name='City of Charlottesville Boundary',
                   style_function=style_function2,
                         tooltip=folium.GeoJsonTooltip(['NAME','% NORM DIFF AVG','% NORM DIFF MED'])).add_to(m.m2)

    
    # 8. Group: Addition of Markers for each individual Parcel
    percMarkers = folium.FeatureGroup(name='% Diff Markers',show=False)
    table.apply(addMarkersPerc,axis=1,args=(m.m1,False,percMarkers))
    percMarkers.add_to(m.m1)
  
    percMarkersNorm = folium.FeatureGroup(name='% Diff Norm Markers',show=False)
    table.apply(addMarkersPerc,axis=1,args=(m.m2,True,percMarkersNorm))
    percMarkersNorm.add_to(m.m2)
  

    # 9. Group: Choropleth of Planning Neighborhood Regions' Average Sale
    choroPerc1 = folium.Choropleth(geo_data=regionlayer, key_on='feature.properties.OBJECTID',
                      data=percByRegionAvg,columns=['Regions','Avg'],
                      fill_color='YlOrRd',legend_name='% Diff',name='% Diff Average', show=False,
                      highlight=True).add_to(m.m1)
    choroPerc1.geojson.add_child(folium.GeoJsonTooltip(['OBJECTID','NAME','% DIFF AVG','NUM PARCELS']))
    
    choroPercNorm1 = folium.Choropleth(geo_data=regionlayer, key_on='feature.properties.OBJECTID',
                  data=percNormByRegionAvg,columns=['Regions','Avg'],
                  fill_color='YlOrRd',legend_name='% Norm Diff',name='% Norm Diff Average', show=False,
                  highlight=True).add_to(m.m2)
    choroPercNorm1.geojson.add_child(folium.GeoJsonTooltip(['OBJECTID','NAME','% NORM DIFF AVG','NUM PARCELS']))
    
    # 10. Group: Choropleth of Planning Neighborhood Regions' Median Sale
    choroPerc2 = folium.Choropleth(geo_data=regionlayer, key_on='feature.properties.OBJECTID',
                      data=percByRegionMed,columns=['Regions','Med'],
                      fill_color='YlOrRd',legend_name='% Diff',name='% Diff Median', show=False,
                      highlight=True).add_to(m.m1)
    choroPerc2.geojson.add_child(folium.GeoJsonTooltip(['OBJECTID','NAME','% DIFF MED','NUM PARCELS']))

    choroPercNorm2 = folium.Choropleth(geo_data=regionlayer, key_on='feature.properties.OBJECTID',
                      data=percNormByRegionMed,columns=['Regions','Med'],
                      fill_color='YlOrRd',legend_name='% Norm Diff',name='% Norm Diff Median', show=False,
                      highlight=True).add_to(m.m2)
    choroPercNorm2.geojson.add_child(folium.GeoJsonTooltip(['OBJECTID','NAME','% NORM DIFF MED','NUM PARCELS']))
 

    # 11. Add LayerControl and Save Map
    folium.LayerControl().add_to(m)
    m.save("figures/map-percentchange.html")
    return 

generateMapPercent(geoSalesResidPercent.copy())

	RecordID_Int	ParcelNumber	SaleDate	SaleAmount	StreetName	StreetNumber	Unit
0	1	010001000	2000/10/23 00:00:00+00	0	EMMET ST N	1117	NaN
1	2	010001000	2016/02/10 00:00:00+00	0	EMMET ST N	1117	NaN
2	3	010001100	2008/09/23 00:00:00+00	0	MILLMONT ST	1035-67	NaN
3	4	010001100	2008/09/23 00:00:00+00	0	MILLMONT ST	1035-67	NaN
4	5	010001100	2008/09/23 00:00:00+00	0	MILLMONT ST	1035-67	NaN
...	...	...	...	...	...	...	...
56291	56277	610318000	2016/12/05 00:00:00+00	0	MILFORD TER	110	NaN
56292	56278	610318000	1999/10/21 00:00:00+00	81500	MILFORD TER	110	NaN
56293	56279	610318000	2017/03/07 00:00:00+00	148000	MILFORD TER	110	NaN
56294	56280	610318000	2003/03/03 00:00:00+00	116900	MILFORD TER	110	NaN
56295	56281	610318000	2013/01/22 00:00:00+00	0	MILFORD TER	110	NaN

	RecordID_Int	ParcelNumber	SaleDate	SaleAmount	Unit	Address
0	1	010001000	2000/10/23 00:00:00+00	0	NaN	1117 EMMET ST N
1	2	010001000	2016/02/10 00:00:00+00	0	NaN	1117 EMMET ST N
2	3	010001100	2008/09/23 00:00:00+00	0	NaN	1035-67 MILLMONT ST
3	4	010001100	2008/09/23 00:00:00+00	0	NaN	1035-67 MILLMONT ST
4	5	010001100	2008/09/23 00:00:00+00	0	NaN	1035-67 MILLMONT ST
...	...	...	...	...	...	...
56291	56277	610318000	2016/12/05 00:00:00+00	0	NaN	110 MILFORD TER
56292	56278	610318000	1999/10/21 00:00:00+00	81500	NaN	110 MILFORD TER
56293	56279	610318000	2017/03/07 00:00:00+00	148000	NaN	110 MILFORD TER
56294	56280	610318000	2003/03/03 00:00:00+00	116900	NaN	110 MILFORD TER
56295	56281	610318000	2013/01/22 00:00:00+00	0	NaN	110 MILFORD TER

	RecordID_Int	ParcelNumber	SaleDate	SaleAmount	Unit	Address
0	1	010001000	2000-10-23	0	NaN	1117 EMMET ST N
1	2	010001000	2016-02-10	0	NaN	1117 EMMET ST N
2	3	010001100	2008-09-23	0	NaN	1035-67 MILLMONT ST
3	4	010001100	2008-09-23	0	NaN	1035-67 MILLMONT ST
4	5	010001100	2008-09-23	0	NaN	1035-67 MILLMONT ST
...	...	...	...	...	...	...
56291	56277	610318000	2016-12-05	0	NaN	110 MILFORD TER
56292	56278	610318000	1999-10-21	81500	NaN	110 MILFORD TER
56293	56279	610318000	2017-03-07	148000	NaN	110 MILFORD TER
56294	56280	610318000	2003-03-03	116900	NaN	110 MILFORD TER
56295	56281	610318000	2013-01-22	0	NaN	110 MILFORD TER

	RecordID_Int	ParcelNumber	StreetNumber	StreetName	Unit	UseCode	Style	Grade	Roof	Flooring	...	Bedrooms	HalfBathrooms	FullBathrooms	BasementGarage	Basement	FinishedBasement	BasementType	ExternalWalls	NumberOfStories	SquareFootageFinishedLiving
0	1	010001600	600	MASSIE RD	NaN	Multipurpose Bldg (College)	2 Story	A	Metal	None	...	4	0	4	0	0.0	0.0	No Basement	Wood	2.00	3672.0
1	2	010005000	0	BARRACKS RD	NaN	Vacant Land	NaN	NaN	NaN	NaN	...	NaN	NaN	NaN	NaN	0.0	0.0	NaN	NaN	NaN	NaN
2	3	010006000	2028	BARRACKS RD	NaN	Single Family	2 Story	A	Architectural Shingles	Hardwood	...	6	1	4	0	1018.0	900.0	Partial Basement	Brick Veneer	2.00	3368.0
3	4	010007000	1930	BARRACKS RD	NaN	Single Family	2 Story	A	Slate	Hardwood	...	5	1	4	0	1708.0	1468.0	Partial Basement	Brick Veneer	2.00	5446.0
4	5	010008000	0	BARRACKS RD	NaN	Vacant Land	NaN	NaN	NaN	NaN	...	NaN	NaN	NaN	NaN	0.0	0.0	NaN	NaN	NaN	NaN
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
14669	14674	610314000	102	MILFORD TER	NaN	Single Family Attached	2 Sty Attached	C	Architectural Shingles	W/W Carpet	...	3	1	1	0	NaN	NaN	No Basement	Vinyl	2.00	1200.0
14670	14675	610315000	104	MILFORD TER	NaN	Single Family Attached	2 Sty Attached	C	Architectural Shingles	W/W Carpet	...	3	1	2	0	NaN	NaN	No Basement	Vinyl	2.00	1200.0
14671	14676	610316000	106	MILFORD TER	NaN	Single Family Attached	2 Sty Attached	C	Architectural Shingles	W/W Carpet	...	3	1	1	0	NaN	NaN	No Basement	Vinyl	2.00	1200.0
14672	14677	610317000	108	MILFORD TER	NaN	Single Family Attached	2 Sty Attached	C	Architectural Shingles	W/W Carpet	...	3	1	1	0	NaN	NaN	No Basement	Vinyl	2.00	1200.0
14673	14678	610318000	110	MILFORD TER	NaN	Single Family Attached	1.5 Sty Attached	C	Architectural Shingles	W/W Carpet	...	3	0	2	0	NaN	NaN	No Basement	Vinyl	1.75	1080.0

	RecordID_Int	ParcelNumber	SaleDate	SaleAmount	Unit	Address	UseCode
0	60	010006000	1995-05-25	0	NaN	2028 BARRACKS RD	Single Family
1	61	010006000	2003-02-20	0	NaN	2028 BARRACKS RD	Single Family
2	62	010006000	2003-01-31	545000	NaN	2028 BARRACKS RD	Single Family
3	63	010007000	1982-07-27	160000	NaN	1930 BARRACKS RD	Single Family
4	66	010009000	1984-08-31	175000	NaN	1920 BARRACKS RD	Single Family
...	...	...	...	...	...	...	...
48052	56277	610318000	2016-12-05	0	NaN	110 MILFORD TER	Single Family Attached
48053	56278	610318000	1999-10-21	81500	NaN	110 MILFORD TER	Single Family Attached
48054	56279	610318000	2017-03-07	148000	NaN	110 MILFORD TER	Single Family Attached
48055	56280	610318000	2003-03-03	116900	NaN	110 MILFORD TER	Single Family Attached
48056	56281	610318000	2013-01-22	0	NaN	110 MILFORD TER	Single Family Attached

Affordable Housing Survey in City of Charlottesville¶

Allen Lang¶

Outline ¶

1 Introduction ¶

2 Data Preprocessing ¶

2.1 Reading/Modifying Sales Table ¶

2.2 Merging Sales with Residential ¶

2.3 Merging with Geocoded Addresses ¶

3 Basic Visualization ¶

4 Classification into Neighborhood Planning Areas ¶

4.1 GeoJSON Layers ¶

4.2 Point in Polygon Algorithm ¶

4.3 Grouping the points ¶

4.4 Creation of the Map ¶

5 Trends over Time ¶

5.1 Aggregating Sales by Region and by Time ¶

5.2 Timeslider Choropleth ¶

5.3 Heat Map with Time ¶

6: Sales History Aggregation ¶

6.1 Creating a Sale History dictionary ¶

6.2 Which Parcels have increasing sales?¶

6.3 Producing a Map of percent changes ¶

	1	2	3	4	5	6	7	8	9	10	11	12	13	14	15	16	17	18	19
2000	191682.295918	163437.757143	94510.021739	1.393247e+05	190208.817308	147265.789474	343734.756944	81931.956522	134304.758621	79317.235294	66625.000000	63294.444444	140800.000000	113526.387097	291186.521739	176813.521739	64257.000000	5.794000e+04	203978.750000
2001	195886.862385	200869.136986	112521.763636	1.518983e+05	218400.581633	155957.352941	386299.730667	85457.993151	134239.923077	86603.740000	110785.714286	72625.000000	149693.870968	158362.333333	367338.333333	196208.068966	61366.666667	6.319655e+04	224007.747748
2002	229277.091743	200951.656716	172194.412500	1.786372e+05	247598.160920	150967.647059	123645.382838	99482.505435	167643.269231	166284.986111	187062.500000	79500.000000	173618.864865	190747.368421	413411.428571	199911.323077	72563.743590	7.517257e+04	256748.035714
2003	293774.490566	220263.453125	192404.141176	2.080533e+05	263562.442308	162707.047619	160023.058047	125572.947368	202458.870968	316928.093750	158633.333333	89500.000000	194377.650602	191664.592593	420882.857143	308849.624000	96667.804348	1.086933e+05	298563.664596
2004	329402.792453	278552.917647	164461.024691	2.344181e+05	289768.088496	194664.866667	192412.155612	153970.613757	221072.000000	324567.000000	123742.857143	106700.000000	219436.564593	282367.849315	446246.666667	363026.153153	130432.523810	1.290700e+05	342107.551020
2005	426188.960938	286630.454545	167020.134831	2.755594e+05	347167.045455	191624.068966	217297.821839	198988.218254	249984.560000	482439.934211	170333.333333	136355.555556	249690.024590	348068.765432	650704.166667	324567.159091	153823.477273	2.107688e+05	466759.200000
2006	479808.366071	326243.408451	198250.705882	2.962844e+05	365526.282051	157190.476190	228236.424437	315157.083019	346571.613757	499610.224599	187500.000000	138462.500000	272561.179104	328948.595745	940394.777778	845618.597938	612502.659574	1.144225e+06	543254.440000
2007	454656.623377	441204.406250	349346.316667	2.715699e+05	368487.084507	192095.428571	227287.531746	370066.811765	417076.501779	340181.672897	224211.000000	135250.000000	286244.118056	322284.523256	956710.600000	986335.367089	657535.476190	1.320538e+06	453832.204819
2008	424802.409091	434232.980392	372297.425532	2.546897e+05	345295.474576	211743.142857	219641.811159	186027.587786	431583.893491	293939.451923	221418.444444	146500.000000	275523.310606	306750.153061	480312.500000	395742.214286	152257.413793	1.428750e+05	417609.866667
2009	460578.097222	321978.756098	390018.485714	2.433003e+05	316922.368421	195049.000000	204029.867925	198929.500000	649794.384615	282908.325581	238329.111111	196000.000000	248864.745614	228124.065574	431433.500000	413963.019608	178393.750000	1.198790e+05	454004.113924
2010	497340.252525	351269.960784	358187.975000	2.317833e+05	315284.702703	164056.294118	188015.522727	227141.206731	848224.317073	268250.101449	258637.428571	214285.714286	237385.200000	199784.020833	413252.500000	396390.309524	187953.411765	8.571322e+04	490299.913043
2011	460955.980000	319574.968750	218509.476190	2.547875e+05	306011.689655	268002.913043	185890.656250	204060.103627	278248.473684	196566.695652	231000.000000	175946.666667	268075.053333	200193.740000	502326.315789	352747.850000	163675.296296	8.042573e+04	491891.779412
2012	420658.234694	313761.477612	221777.229167	2.554558e+05	308824.243902	284040.000000	201973.798419	170076.484472	259775.230769	197032.186813	266350.000000	122795.000000	278293.507614	212232.596491	534094.117647	386340.179104	142012.875000	1.044241e+05	433036.516854
2013	458520.567010	369592.786885	213980.024390	2.365795e+05	327563.000000	174065.166667	219900.988722	175690.820000	270205.818182	221882.442623	279620.000000	90500.000000	258260.692308	260129.777778	543196.466667	437618.711864	159847.185185	1.328353e+05	413494.467290
2014	481614.744186	369444.642857	277469.650000	2.626569e+05	339083.797468	192233.650000	239887.590164	186566.253623	308276.923077	248680.465839	273200.000000	161200.000000	277480.921296	245438.185714	605087.350000	389784.857143	176192.590909	1.934001e+05	411682.684685
2015	496539.625000	361467.213115	283404.280000	1.131422e+06	337120.517241	224104.545455	242761.353383	215265.649007	325096.000000	264166.722772	271650.000000	148100.000000	284277.179245	252323.639535	604076.190476	408567.148148	204481.285714	2.016584e+05	456843.147826
2016	703177.943396	431646.098765	275338.264151	9.857613e+05	363238.242105	219387.500000	264642.306050	225238.286550	328296.613636	280779.705556	403500.000000	135000.000000	321729.798354	301478.051948	612394.117647	551844.365079	239798.600000	1.863022e+05	529127.729167
2017	866624.000000	484094.019048	282198.044444	3.519668e+05	421612.043478	207086.363636	306167.406557	233705.700000	349164.152174	298332.752212	540000.000000	105000.000000	339523.180451	301986.506024	678141.846154	667565.425926	223282.031250	2.056954e+05	619536.436170
2018	793910.216867	498730.402062	299359.431818	3.616255e+05	445853.258427	217056.900000	315554.310345	241290.400000	361766.381818	293732.452381	750000.000000	105000.000000	320908.758065	323024.977778	746966.347826	654373.122449	224651.950000	1.982917e+05	626220.891089
2019	717857.860465	511822.775000	314556.120000	3.681375e+05	441875.581395	200313.800000	314220.600000	241635.603448	357772.259259	292720.743590	750000.000000	105000.000000	328184.814516	358465.073171	767669.529412	586843.750000	278134.142857	2.076667e+05	563986.363636

	RecordID_Int	ParcelNumber	SaleDate	SaleAmount	Unit	Address	UseCode	LATITUDE	LONGITUDE	finalincr	perc	percNorm
24	86	010017000	2017-06-08	636000	NaN	1893 WESTVIEW RD	Single Family-1 Conversion	38.050093	-78.499944	True	61.949686	0.124397
31	93	010017100	2000-09-14	459500	NaN	1895 WESTVIEW RD	Single Family-2 Conversion	38.050473	-78.499333	True	42.491839	0.022566
36	99	010019000	2002-05-23	85000	NaN	1890 WESTVIEW RD	Single Family	38.050086	-78.500280	True	684.285714	0.276927
43	106	010020000	2010-08-04	962500	NaN	1888 WESTVIEW RD	Single Family	38.049628	-78.500349	True	250.000000	0.071860
62	125	010024A00	2002-06-12	125000	NaN	1876 WESTVIEW RD	Single Family	38.048490	-78.499412	True	652.000000	0.148148
...	...	...	...	...	...	...	...	...	...	...	...	...
47995	56251	610313000	2001-03-23	83000	NaN	100 MILFORD TER	Single Family Attached	38.018602	-78.470619	True	116.746988	0.074790
48000	56256	610314000	2002-07-31	117900	NaN	102 MILFORD TER	Single Family Attached	38.018528	-78.470644	True	6.022053	0.001819
48005	56261	610315000	2005-11-01	174900	NaN	104 MILFORD TER	Single Family Attached	38.018473	-78.470647	True	6.346484	0.007005
48013	56269	610317000	2009-12-14	102128	NaN	108 MILFORD TER	Single Family Attached	38.018362	-78.470659	True	19.947517	0.189976
48019	56275	610318000	2001-07-20	89900	NaN	110 MILFORD TER	Single Family Attached	38.018268	-78.470688	True	64.627364	0.011320

Affordable Housing Survey in City of Charlottesville¶

Allen Lang¶

Outline¶

1 Introduction¶

2 Data Preprocessing¶

2.1 Reading/Modifying Sales Table¶

2.2 Merging Sales with Residential¶

2.3 Merging with Geocoded Addresses¶

3 Basic Visualization¶

4 Classification into Neighborhood Planning Areas¶

4.1 GeoJSON Layers¶

4.2 Point in Polygon Algorithm¶

4.3 Grouping the points¶

4.4 Creation of the Map¶

5 Trends over Time¶

5.1 Aggregating Sales by Region and by Time¶

5.2 Timeslider Choropleth¶

5.3 Heat Map with Time¶

6: Sales History Aggregation¶

6.1 Creating a Sale History dictionary¶

6.2 Which Parcels have increasing sales?¶

6.3 Producing a Map of percent changes¶

Outline ¶

1 Introduction ¶

2 Data Preprocessing ¶

2.1 Reading/Modifying Sales Table ¶

2.2 Merging Sales with Residential ¶

2.3 Merging with Geocoded Addresses ¶

3 Basic Visualization ¶

4 Classification into Neighborhood Planning Areas ¶

4.1 GeoJSON Layers ¶

4.2 Point in Polygon Algorithm ¶

4.3 Grouping the points ¶

4.4 Creation of the Map ¶

5 Trends over Time ¶

5.1 Aggregating Sales by Region and by Time ¶

5.2 Timeslider Choropleth ¶

5.3 Heat Map with Time ¶

6: Sales History Aggregation ¶

6.1 Creating a Sale History dictionary ¶

6.3 Producing a Map of percent changes ¶